Machine Learning
Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.
3418papers
Papers - Page 61
May 29, 2024
May 28, 2024
Counterfactual Explanations for Multivariate Time-Series without Training Datasets
NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning
Unlocking Futures: A Natural Language Driven Career Prediction System for Computer Science and Software Engineering Students
Is machine learning good or bad for the natural sciences?
Design Principles for Falsifiable, Replicable and Reproducible Empirical ML Research
May 27, 2024
A Retrospective of the Tutorial on Opportunities and Challenges of Online Deep Learning
Forecasting Four Business Cycle Phases Using Machine Learning: A Case Study of US and EuroZone
Performance evaluation of Reddit Comments using Machine Learning and Natural Language Processing methods in Sentiment Analysis
May 26, 2024
May 25, 2024
May 24, 2024
Improving Simulation Regression Efficiency using a Machine Learning-based Method in Design Verification
Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models
Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data